Wsrtjtyk

My program divides my dataset into train and test set, builds a decision tree based on the train and test set and calculates the accuracy, sensitivity and the specifity of the confusion matrix.

I added a for loop to rerun my program 100 times. This means I get 100 train and test sets. The output of the for loop is a result_df with columns of accuracy, specifity and sensitivity.

This is the for loop:

result_df<-matrix(ncol=3,nrow=100)

colnames(result_df)<-c("Acc","Sens","Spec")



for (g in 1:100 )

{



  # Divide into Train and test set

  smp_size <- floor(0.8 * nrow(mydata1))

  train_ind <- sample(seq_len(nrow(mydata1)), size = smp_size)

  train <- mydata1[train_ind, ]

  test <- mydata1[-train_ind, ]



  REST OF MY CODE



}

My result_df (first 20 rows) looks like this:

> result_df[1:20,]

   Acc Sens Spec id

1   26   22   29  1

2   10   49   11  2

3   37   43   36  3

4    4   79    4  4

5   21   21   20  5

6   31   17   34  6

7   57    4   63  7

8   33    3   39  8

9   56   42   59  9

10  65   88   63 10

11   6   31    7 11

12  57   44   62 12

13  25   10   27 13

14  32   24   32 14

15  19    8   19 15

16  27   27   29 16

17  38   89   33 17

18  54   32   56 18

19  35   62   33 19

20  37    6   40 20

I use ggplot() to plot the specifity and the sensitivity as a scatterplot:

What I want to do :

I want to see e.g. the train and test set of datapoint 17.

I think I can do this by using the set.seed function, but I am very unfamiliar with this function.

First, clearly, if in your code you store your estimate models, e.g., in a list, then you could recover your data from those models. However, it doesn't look like that's the case.

With your current code all you can do is to see that last train and test sets (number 100). That is because you keep redefining test, train, train_ind variables. The cheapest (in terms of memory) way to achieve what you want would be to somehow store train_ind from each iteration. For instance, you could use

train_inds <- list()[rep(1, 100)]

for (g in 1:100 )

{

  smp_size <- floor(0.8 * nrow(mydata1))

  train_inds[[g]] <- sample(seq_len(nrow(mydata1)), size = smp_size)

  train <- mydata1[train_inds[[g]], ]

  test <- mydata1[-train_ind[[g]], ]

  # The rest

}

and in this way you would always know which observations were in which set. If you somehow are interested only in one specific iteration, you could save only that one.

Lastly, set.seed isn't really going to help here. If all you were doing was running rnorm(1) hundred times, then yes, by using set.seed you could quickly recover the n-th generated value later. In your case, however, you are not only using sample for train_ind; the model estimation functions are also very likely generating random values.